Optimizing Cardiovascular Disease Prediction: A Synergistic Approach of Grey Wolf Levenberg Model and Neural Networks
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Background: One of the latest issues in predicting cardiovascular disease is the limited performance of current risk prediction models. Although several models have been developed, they often fail to identify a significant proportion of individuals who go on to develop the disease. This highlights the need for more accurate and personalized prediction models.
Objective: This study aims to investigate the effectiveness of the Grey Wolf Levenberg Model and Neural Networks in predicting cardiovascular diseases. The objective is to identify a synergistic approach that can improve the accuracy of predictions. Through this research, the authors seek to contribute to the development of better tools for early detection and prevention of cardiovascular diseases.
Methods: The study used a quantitative approach to develop and validate the GWLM_NARX model for predicting cardiovascular disease risk. The approach involved collecting and analyzing a large dataset of clinical and demographic variables. The performance of the model was then evaluated using various metrics such as accuracy, sensitivity, and specificity.
Results: the study found that the GWLM_NARX model has shown promising results in predicting cardiovascular disease. The model was found to outperform other conventional methods, with an accuracy of over 90%. The synergistic approach of Grey Wolf Levenberg Model and Neural Networks has proved to be effective in predicting cardiovascular disease with high accuracy.
Conclusion: The use of the Grey Wolf Levenberg-Marquardt Neural Network Autoregressive model (GWLM-NARX) in conjunction with traditional learning algorithms, as well as advanced machine learning tools, resulted in a more accurate and effective prediction model for cardiovascular disease. The study demonstrates the potential of machine learning techniques to improve diagnosis and treatment of heart disorders. However, further research is needed to improve the scalability and accuracy of these prediction systems, given the complexity of the data associated with cardiac illness.
Keywords: Cardiovascular data, Clinical data., Decision tree, GWLM-NARX, Linear model functions
R.G. Nadakinamani, A. Reyana, S. Kautish, A.S. Vibith, Y. Gupta, S.F. Abdelwahab, and A.W. Mohamed, “Clinical Data Analysis for Prediction of Cardiovascular Disease Using Machine Learning Techniques,” Comput. Intell. Neurosci., 2973324, pp. 1-13, 2022.
I. Altaf, M.A. Butt, and M. Zaman, “Disease detection and prediction using the liver function test data: A review of machine learning algorithms,” in International Conference on Innovative Computing and Communications, 2022, pp. 785-800.
Rahim, Aqsa, Y. Rasheed, F. Azam, M.W. Anwar, M.A. Rahim, and A.W. Muzaffar, “An integrated machine learning framework for effective prediction of cardiovascular diseases,” IEEE Access, vol. 9, pp. 106575-106588, 2021.
A. Javeed, S.U. Khan, L. Ali, S. Ali, Y. Imrana, and A. Rahman, “Machine learning-based automated diagnostic systems developed for heart failure prediction using different types of data modalities: A systematic review and future directions,” Comput. Math. Methods Med., 9288452, pp. 1-30, 2022. https://doi.org/10.1155/2022/9288452
I. Altaf, M.A. Butt, and M. Zaman, “Hard Voting Meta Classifier for Disease Diagnosis using Mean Decrease in Impurity for Tree Models,” Rev. Comput. Eng. Res., vol. 9, no. 2, pp. 71-82, 2022.
A.S.M. Faizal, T.M. Thevarajah, S.M. Khor, and S.W. Chang, “A review of risk prediction models in cardiovascular disease: conventional approach vs. artificial intelligent approach,” Comput. Methods Programs Biomed., vol. 207, 106190.
B. Martins, D. Ferreira, C. Neto, A. Abelha, and J. Machado, “Data mining for cardiovascular disease prediction,” J. Med. Syst., vol. 45, no. 1, pp. 1-8, 2021.
K. Rajmohan, I. Paramasivam, and S. Sathyanarayan, “Prediction and Diagnosis of Cardio Vascular Disease-A Critical Survey,” in World Conference on Computing and Communication Technologies, pp. 246-251, 2014.
H.E. Bays, P.R. Taub, E. Epstein, E.D. Michos, R.A. Ferraro, A.L. Bailey, H.M. Kelli, “Ten things to know about ten cardiovascular disease risk factors,” American Journal of Preventive Cardiology, vol. 5, 100149, 2021.
B. Martins, D. Ferreira, C. Neto, A. Abelha, and J. Machado, “Data mining for cardiovascular disease prediction,” J. Med. Syst., vol. 45, no. 1, pp. 1-8, 2021.
V. Dilsizian, H. Gewirtz, T.H. Marwick, R.Y. Kwong, P. Raggi, M.H. Al-Mallah, and C.A. Herzog, “Cardiac imaging for coronary heart disease risk stratification in chronic kidney disease,” JACC: Cardiovascular Imaging, vol. 14, no. 3, pp. 669-682, 2021.
S.A. Fayaz, M. Zaman, and M.A. Butt, “To ameliorate classification accuracy using ensemble distributed decision tree (DDT) vote approach: an empirical discourse of geographical data mining,” Procedia Comput. Sci., vol. 184, pp. 935-940, 2021.
M. Ashraf, Y.K. Salal, S.M. Abdullaev, M. Zaman, and M.A. Bhut, “Introduction of feature selection and leading-edge technologies viz. tensorflow, pytorch, and keras: An empirical study to improve prediction accuracy of cardiovascular disease,” in International Conference on Innovative Computing and Communications, pp. 19-31, 2022.
S.A. Fayaz, M. Zaman, and M.A. Butt, “An application of logistic model tree (LMT) algorithm to ameliorate prediction accuracy of meteorological data,” Int. J. Adv. Technol. Eng. Explor., vol. 8, no. 84, pp. 1424-1440, 2021.
S. Cristoforo, S. Rospleszcz, J.C. Kaiser, and K. Furukawa, “Heterogeneity in coronary heart disease risk,” Sci. Rep., vol. 12, no. 1 pp. 1-9, 2022.
S.P. Rajamhoana, C.A. Devi, K. Umamaheswari, R. Kiruba, K. Karunya, and R. Deepika, “Analysis of neural networks based heart disease prediction system,” in 11th International Conference on Human System Interaction, pp. 233-239, 2018.
S. Babu, E.M. Vivek, K.P. Famina, K. Fida, P. Aswathi, M. Shanid, and M. Hena, “Heart disease diagnosis using data mining technique,” in International Conference of Electronics, Communication and Aerospace, vol. 1, pp. 750-753, 2017.
C. Krittanawong, H.U.H. Virk, S. Bangalore et al., “Machine learning prediction in cardiovascular diseases: a meta-analysis,” Sci. Rep., vol. 10, no. 1, pp. 16057-16111, 2020.
G. Lippi, B.M. Henry, and F. Sanchis-Gomar, “Physical inactivity and cardiovascular disease at the time of coronavirus disease 2019 (COVID-19),” Eur. J. Prev. Cardiol., vol. 27, no. 9, pp. 906-908, 2020.
S. Aryal, A. Alimadadi, I. Manandhar, B. Joe, and X. Cheng, “Machine learning strategy for gut microbiome-based diagnostic screening of cardiovascular disease,” Hypertension, vol. 76, no. 5, pp. 1555-1562, 2020.
I.K.A. Enriko, M. Suryanegara, and D. Gunawan, “Heart disease diagnosis system with K-nearest neighbors method using real clinical medical records,” in Proc. 4th Int. Conf. Frontiers Educ. Technol., pp. 127-131, 2018.
E. B. Randa, “An ensemble model for heart disease data sets: A generalized model,'' in Proc. 10th Int. Conf. Inform. Syst., pp. 191196, 2016.
S. Song, J. Warren, and P. Riddle, “Developing high risk clusters for chronic disease events with classi_cation association rule mining,” in Proc. 7th Australas. Workshop Health Inform. Knowl. Manage., vol. 153, pp. 69-78, 2014.
A. Javeed, S.S. Rizvi, S. Zhou, R. Riaz, S.U. Khan, and S.J. Kwon, “Heart risk failure prediction using a novel feature selection method for feature refinement and neural network for classification,” Mob. Inf. Syst., 8843115, pp. 1-11, 2020. https://doi.org/10.1155/2020/8843115
T. Sharma, S. Verma, and Kavita, “Prediction of heart disease using Cleveland dataset: A machine learning approach,” Int. J. Recent Res. Aspects, vol. 4, no. 3, 1721, 2017.
S.J. Al’Aref, K. Anchouche, G. Singh, “Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging,” Eur. Heart J., vol. 40, no. 24, pp. 1975-1986, 2019.
A.H. Gonsalves, F. Thabtah, R.M.A. Mohammad, and G. Singh, “Prediction of coronary heart disease using machine learning: An experimental analysis,” in Proc. 3rd Int. Conf. Deep Learn. Technol., pp. 51-56, 2019.
H.A.G. Elsayed and L. Syed, “An automatic early risk classification of hard coronary heart diseases using Framingham scoring model,” in Proc. 2nd Int. Conf. Internet Things, Data Cloud Comput., pp. 1-8, 2017.
S. Mohan, C. Thirumalai, and G. Srivastava, “Effective heart disease prediction using hybrid machine learning techniques,” IEEE Access, vol. 7, pp. 81542-81554, 2019.
L. Yang, H. Wu, X. Jin, P. Zheng, S. Hu, X. Xu, W. Yu, and J. Yan, “Study of cardiovascular disease prediction model based on random forest in eastern China,” Sci. Rep., vol. 10, no. 1, pp. 1-8, 2020.
M. Ashraf, S.M. Ahmad, N.A. Ganai, R.A. Shah, M. Zaman, S.A. Khan, and A.A. Shah, “Prediction of cardiovascular disease through cutting-edge deep learning technologies: an empirical study based on tensorflow, pytorch and keras,” in International Conference on Innovative Computing and Communications, pp. 239-255, 2021.
C. Perrino, P. Ferdinandy, H.E. Bøtker, B.J. Brundel, P. Collins, S.M. Davidson, and K. Ytrehus, “Improving translational research in sex-specific effects of comorbidities and risk factors in ischaemic heart disease and cardioprotection: position paper and recommendations of the ESC Working Group on Cellular Biology of the Heart,” Cardiovasc. Res., vol. 117, no. 2, pp. 367-385, 2021.
A. Voutilainen, C. Brester, M. Kolehmainen, and T.P. Tuomainen, “Effects of data preprocessing on results of the epidemiological analysis of coronary heart disease and behaviour-related risk factors,” Ann. Med., vol. 53, no. 1, pp. 890-899, 2021.
S.A. Fayaz, S. Kaul, M. Zaman, and M.A. Butt, “An adaptive gradient boosting model for the prediction of rainfall using ID3 as a base estimator,” Revue d'Intelligence Artificielle, vol. 36, no. 2, pp. 241-250, 2022. https://doi.org/10.18280/ria.360208
M. Kenny, and I. Schoen, “Violin SuperPlots: Visualizing replicate heterogeneity in large data sets,” Molecular Biology of the Cell, vol. 32, no. 15, pp. 1333-1334, 2022.
B,A. Burton, B. Datta, and J. Spreer, “Flip graphs of stacked and flag triangulations of the 2-sphere,” Electron. J. Comb., vol. 29, no. 2, PP. 1-30, 2022.
S.L. Turner, A. Karahalios, A.B. Forbes, M. Taljaard, J.M. Grimshaw, E. Korevaar, A.C. Cheng, L. Bero, and J.E. McKenzie, “Creating effective interrupted time series graphs: review and recommendations,” Res. Synth. Methods, vol. 12, no. 1, pp. 106-117, 2021.
S. Pirzada, and A. Altaf, “Line graphs of unit graphs associated with the direct product of rings,” Korean J. Math., vol. 30, no. 1, pp. 53-60, 2022.
R. Mohd, M.A. Butt, and M.Z. Baba, “SALM-NARX: Self Adaptive LM-based NARX model for the prediction of rainfall,” in 2nd international conference on I-SMAC (IoT in social, mobile, analytics and cloud), pp. 580-585, 2018.
A.N. Nowbar, M. Gitto, J.P. Howard, D.P. Francis, and R. Al-Lamee, “Mortality from ischemic heart disease: Analysis of data from the World Health Organization and coronary artery disease risk factors From NCD Risk Factor Collaboration,” Circulation: cardiovascular quality and outcomes, vol. 12, no. 6, e005375, 2019.
J.Z.K. Toh, X.H. Pan, P.W.L. Tay, C.H. Ng, J.N. Yong, J. Xiao, and J.H. Koh, “A meta-analysis on the global prevalence, risk factors and screening of coronary heart disease in nonalcoholic fatty liver disease,” Clinical Gastroenterology and Hepatology, vol. 20, no. 11, pp. 2462-2473, 2021.
S.A. Fayaz, M. Zaman, and M.A. Butt, “A hybrid adaptive grey wolf Levenberg-Marquardt (GWLM) and nonlinear autoregressive with exogenous input (NARX) neural network model for the prediction of rainfall,” Int. J. Adv. Technol. Eng. Explor., vol. 9, no. 89, pp. 511-524, 2022.
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